The Elements Of Statistical Learning By Robert Tibshirani Hardcover

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size

23.62 x 15.24 x 3.56 cm

Product Information

Brand Name

Books Kingdom

Ideal For

General Readers, Fiction Lovers, Mythology & Historical Fiction Readers, Indian Mythology Enthusiasts, Young Adults (YA) & Adults, College Students, Competitive Exam Aspirants (General Reading / Culture), Gift Purpose (Books Gift Set), Spiritual & Philosophy Interest Readers, Library / Book Collection Buyers

Material

Hardcover

Country Of Origin

India

Pack Of

About Author

Robert Tibshirani (Born July 10, 1956) Is A Professor In The Departments Of Statistics And Health Research And Policy At Stanford University. He Was A Professor At The University Of Toronto From 1985 To 1998. In His Work, He Develops Statistical Tools For The Analysis Of Complex Datasets, Most Recently In Genomics And Proteomics.

Author Name

Robert Tibshirani

Genre

Science & Mathematics

Isbn Code

978-0387848570 (Isbn Code), 978-0387848570 (Isbn Code)

No Of Pages

745

Publisher

Anant Pai / Amar Chitra Katha

Product Description

Premium Quality: This hardcover edition of The Elements of Statistical Learning by Robert Tibshirani spans 745 pages and is a valuable resource for statisticians and anyone interested in data mining in science or industry.

Product Design: The book describes important ideas across a variety of fields such as medicine, biology, finance, and marketing within a common conceptual framework, supported by a liberal use of colour graphics.

User Experience: While the approach is statistical, the emphasis is on concepts rather than mathematics, making complex topics accessible through many illustrative examples presented with clarity and rigour.

Versatile Occasion: A broad resource covering both supervised learning and unsupervised learning, suitable for academic study, professional reference, and gifting to data science or statistics practitioners.

Quality Assurance: Authored by Robert Tibshirani, Professor in the Departments of Statistics and Health Research and Policy at Stanford University, and a leading authority on statistical tools for complex datasets.

Lifestyle Essential: Offering the first comprehensive treatment of topics such as neural networks, support vector machines, classification trees, and boosting in any single book, this title is an indispensable reference for the modern data scientist.

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